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Full Stack AI Developer Roadmap
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Module 4
Mathematics for Machine Learning
1
NumPy Array Fundamentals
Create, index, and perform operations on NumPy arrays (vectors and matrices)
2
Matrix Operations: Dot Products, Multiplication, and Transposes
Compute and interpret dot products, matrix multiplication, and transposes
3
Eigenvalues and Eigenvectors: Calculation and Geometric Interpretation
Calculate eigenvalues and eigenvectors and explain their geometric significance
4
Derivatives and Rates of Change
Compute derivatives and partial derivatives; interpret as rates of change
5
Chain Rule for Gradients in Computational Graphs
Apply the chain rule to compute gradients in computational graphs
6
Measures of Central Tendency and Dispersion
Calculate mean, variance, standard deviation, and correlation coefficients
7
Understanding Probability: Sample Spaces, Events, and Conditional Probability
Define probability concepts: sample space, events, and conditional probability
8
Calculating Posterior Probabilities with Bayes' Theorem
Apply Bayes' theorem to compute posterior probabilities
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Web Development with Python
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Data Analysis and Visualization